Object recognition in digital images is used for tracking faces (e.g., for security purposes) and medical imaging. Exemplary shape detection algorithms which may be used for object recognition include Active Appearance Model (AAM) and Active Shape Model (ASM).
The AAM algorithm matches a statistical model of object shapes provided during a training phase, with object shapes in a current image. During training, a set of images and corresponding coordinates for objects in the images is provided stored. Then during use, the algorithm compares the current image with the stored object recognition data. The least squares statistical technique is used to match objects in current image. However, ASM only uses shape constraints for object recognition and does not take advantage of other information such as the texture across the target object.
The ASM algorithm uses statistical models of the shape of objects which iteratively deform to fit to an example of the object in a new image. The shape of an object is represented by a set of points controlled by the shape model. The model is then matched to the current image by alternating between searching in the image around each point for a better position for that point, and updating the model parameters to best match to the new found positions. A better position for each point can be located by finding strong edges or matching to a statistical model of what is expected at the point.
However, the AAM and ASM algorithms are computationally intensive and therefore can be slow unless utilized on large computing systems such as those that might be used in conjunction with large video surveillance and medical imaging systems. The AAM and ASM algorithms have also been found to be error-prone.